Deep Learning Methods for Precise Sugarcane Disease Detection and Sustainable Crop Management

In the agricultural domain, sugarcane crops, like many others, are susceptible to diseases, posing a significant threat to both quality and quantity of production. Identifying and mitigating these diseases in their early stages are critical to averting financial losses for farmers. In response, researchers have turned to Artificial Intelligence (AI) techniques such as Machine Learning (ML) and Deep Learning (DL) to analyze diverse agricultural data, including yield prediction, climate patterns, and soil quality, with disease prevention being a prime focus. This paper presents a thorough exploration of the effectiveness of a Deep Learning-based Convolutional Neural Network (CNN) algorithm tailored for the detection of prevalent sugarcane diseases in India. Motivated by the rapid evolution of disease classes and farmers’ limited diagnostic skills, this study employs advanced deep learning and computer vision techniques. Through image categorization into healthy and diseased groups, the trained model achieves an impressive 98.69% accuracy rate in sugarcane disease detection. Furthermore, to empower farmers, a web-based application is developed for ongoing disease monitoring. The paper suggests future research avenues, including user feedback integration and exploring the intersection of disease detection with agricultural productivity enhancement and price forecasting, thus enriching farmers’ decision-making processes.